This is a problem as I like to have some concept of what it is that I’m watching. I buried my head in baseball stats as a kid, dive into basketball stats year round, and play fantasy football largely without watching football games because I like numbers.

I wondered what I was going to be watching at #NationalsIL and I couldn’t find much of anything. How many throws per game? What’s the average completion percentage? What about Possessions per goal? Which defenses give up the highest completion percentage? Which defenses are most likely to convert their break opportunities? Which teams convert the highest percentage of their “Shots” (aka throws into the endzone)?

I wasn’t concerned so much with individual player stats because without the background knowledge of overall team stats, individual stats are mostly meaningless. I’m also certain that my “eye test” will tell me which players are outstanding and which fall in with “the great unwashed masses”.

So I sought out women’s games from 2016 against club competition (Not against All-Star Teams) and hoped they would be reasonably evenly matched. I went to my team’s Ultiworld subscription and ended up with 16 games. With a few weeks between the end of my coaching season and the start of #NationalsIL, I had a bunch of charting work to do. I charted the 16 games available from Ultiworld and then added 1 game filmed by Georgetown Ultimate at MA Women’s Regionals. Since then, I’ve found more games (from folks like Hallie’s Dad) but simply lacked the time to chart them. The games I charted are as follows (Team which received to start is listed first):

6ixers 13 v 8 Schwa ESC

Nemesis 13 v 5 Heist ESC

Underground 6 v 13 Phoenix ESC

Brute Squad 13 v 12 Molly Brown PFF

Schwa 8 v 13 Brute PFF

Nightlock 10 v 13 Scandal PFF

Scandal 11 v 13 Fury PFF

Traffic 13 v 10 Fury PFF

Brute Squad 15 v 14 Molly Brown PFF

Riot 13 v 11 Brute PFF

Brute 10 v 13 Riot PEC

Riot 13 v 8 Nightlock PEC

Molly Brown 13 v 6 Scandal PEC

Scandal 13 v 15 Molly Brown USO

Fury 11 v 15 Molly Brown USO

Brute Squad 15 v 7 Scandal USO

Scandal 14 v 11 Green Means Go MAR

Teams which received to start the game went 10-7 and outscored their opponents 11.71 to 11.00 per game.

There were 5049 Throws (3633 Off, 1416 Def), 542 of which were Incompletions (230 Off, 212 Def) for an average completion percentage of 89.27% (90.92% Off, 85.03% Def). Of those 5049 Throws, 547 (374 Off, 173 Def) were “shots” (aka “a throw into the endzone”) and of those 547 shots, there were 386 goals (267 Off, 119 Def) yielding a shooting percentage of 70.57% (71.39% Off, 68.79% Def). There were 386 Points Played, 267 resulted in a Hold (69.17%) while 119 resulted in a Break (30.83%). There were 595 Offensive Possessions and 330 Offensive Possessions yielding Off. Poss/G of 2.228 and Def. Poss/G of 2.773. Overall Conversion % of Offensive Possessions were 44.87% while Defensive Possessions were 36.06%.

Offensive teams averaged 6.11 Throws per Possession while Defensive teams averaged 4.29 Throws per Possession (Overall: 5.49 Throws/Poss). Offensive teams average 13.61 Throws per Goal while Defensive teams averaged 11.90 Throws per Goal (Overall 13.08 Thr/G). Offensive teams took a shot at the endzone on 62.86% of their possessions while Defensive teams took a shot at the endzone on 52.42% of their possessions (Overall 59.13% of Possessions ended in a scoring opportunity.)

In the first half of games, there were 204 Goals (52.84% of Total Goals. 152 were Holds [74.51%], 52 were breaks [25.49%]) while in the second half there were 182 Goals (47.16% of Total Goals. 115 were Holds [63.19%], 67 were breaks [36.81%]).

That’s a buncha numbers in paragraph form. Let’s take a look at what the “average game” looks like broken out by halves and by Winning Team (left side) and Losing Team (right side):

H1

2016W

Winner

Loser

H1

Pts

G

EZ

Cmp%

Inc

Thr

Poss

Poss

Thr

Inc

Cmp%

EZ

G

Pts

O

5.24

4.65

5.82

93.28

3.29

49.00

7.88

10.35

60.65

6.12

89.91

6.18

4.29

6.76

D

6.76

2.47

3.47

87.16

3.65

28.41

6.12

3.29

13.24

2.71

79.56

1.24

0.59

5.24

T

12.00

7.12

9.29

91.03

6.94

77.41

14.00

13.65

73.88

8.82

88.06

7.41

4.88

12.00

H2

2016W

Winner

Loser

H2

Pts

G

EZ

Cmp%

Inc

Thr

Poss

Poss

Thr

Inc

Cmp%

EZ

G

Pts

O

4.71

3.59

4.65

92.36

3.65

47.71

7.24

9.53

56.35

6.35

88.73

5.35

3.18

6.00

D

6.00

2.82

3.53

86.26

3.59

26.12

6.35

3.65

15.53

2.53

83.71

1.94

1.12

4.71

T

10.71

6.41

8.18

90.20

7.24

73.82

13.59

13.18

71.88

8.88

87.64

7.29

4.29

10.71

GM

Winner

Loser

GM

Pts

G

EZ

Cmp%

Inc

Thr

Poss

Poss

Thr

Inc

Cmp%

EZ

G

Pts

O

9.94

8.24

10.47

92.82

6.94

96.71

15.12

19.88

117.00

12.47

89.34

11.53

7.47

12.76

D

12.76

5.29

7.00

86.73

7.24

54.53

12.47

6.94

28.76

5.24

81.80

3.18

1.71

9.94

T

22.71

13.53

17.47

90.63

14.18

151.24

27.59

26.82

145.76

17.71

87.85

14.71

9.18

22.71

The first thing to remember when looking at this is just how thin the margin between winning and losing is. The losing team throws 17.71 Incompletions while the winning team throws 14.18 Incompletions. That’s a difference of 3.53 Incompletions per game resulting in a final score differential of 4.35 (13.53 to 9.18 is the average final score.) Of course, that’s a little simplistic, which is why my second thing to look at is a table of simple derived statistics (Top five rows are the winning teams, middle rows are the losing team, bottom rows are the difference from Winning minus Losing):

Win

Possessions

Throws

Conversions

Pos/G

Pos/Pt

Pos/EZ

Thr/G

Th/Pt

Thr/Pos

Thr/EZ

EZ%Th

EZ%Pos

Shot%

Conv%Pos

Conv%Pt

Off.

1.836

1.521

1.444

11.743

9.728

6.397

9.236

10.827

69.261

78.652

54.475

82.840

Def.

2.356

0.977

1.782

10.30

4.272

4.373

7.790

12.837

56.132

75.630

42.453

41.475

Total

2.039

1.215

1.579

11.18

6.661

5.482

8.657

11.552

63.326

77.441

49.041

59.585

Lose

Possessions

Throws

Conversions

Pos/G

Pos/Pt

Pos/EZ

Thr/G

Th/Pt

Thr/Pos

Thr/EZ

EZ%Th

EZ%Pos

Shot%

Conv%Pos

Conv%Pt

Off.

2.661

1.558

1.724

15.66

9.166

5.885

10.15

9.854

57.988

64.796

37.574

58.525

Def.

4.069

0.698

2.185

16.86

2.893

4.144

9.056

11.043

45.763

53.704

24.576

17.160

Total

2.923

1.181

1.824

15.89

6.420

5.434

9.912

10.089

54.825

62.400

34.211

40.415

Diff

Possessions

Throws

Conversions

Pos/G

Pos/Pt

Pos/EZ

Thr/G

Th/Pt

Thr/Pos

Thr/EZ

EZ%Th

EZ%Pos

Shot%

Conv%Pos

Conv%Pt

Off.

-0.826

-0.037

-0.281

-3.919

0.562

0.512

-0.912

0.973

11.273

13.856

16.901

24.315

Def.

-1.713

0.279

-0.404

-6.562

1.378

0.229

-1.266

1.794

10.369

21.927

17.877

24.315

Total

-0.884

0.034

-0.245

-4.706

0.241

0.048

-1.255

1.463

8.502

15.041

14.830

19.171

The things which stand out the most to me are the differences in:

Defensive Possessions per Goal (Winning team at 2.356, losing team at 4.069),

Point Conversion Percentage (Winning teams Hold on 82.84% and Break on 41.475% while losing teams Hold on 58.525% and Break on 17.16%)

With that as a primer, what can we learn about the individual teams involved?

If we consider each team in aggregate (not splitting for O/D or by halves), and each team’s opponents in aggregate, there are some basic notions which emerge from the games considered:

Throws per possession range from 4.034 (Phoenix in 1 game v Underground) to 6.986 (“Riot’s Opponents” in 3 games vs Riot).

For teams with more than 2 games, Throws per possession range from 5.006 (Molly Brown) to 6.848 (Brute Squad)

Completion Percentage ranges from 82.00% (Underground in 1 game v Phoenix) to 93.4% (“Nightlock’s Opponents” in 2 games vs Nightlock).

For teams with more than 2 games, Completion Percentage ranges from 87.8% (“Molly Brown’s Opponents” in 5 games vs Molly Brown) to 92.1% (Brute Squad in 6 games)

Conversion of Possessions ranges from 17.24% (Heist in 1 game v Nemesis) to 65.00% (“Nightlock’s Opponents” in 2 games v Nightlock).

For teams with more than 2 games, Conversion of Possessions ranges from 33.53% (“Molly Brown’s Opponents” in 5 games v Molly Brown) to 52.70% (Riot in 3 games)

Shooting Percentage ranges from 42.11% (“6ixers Opponent” in 1 game v Schwa) to 92.86% (Traffic in 1 game v Fury)

For teams with more than 2 games, Shooting Percentage ranges from 65.17% (“Molly Brown’s Opponents” in 5 games v Molly) to 82.98% (“Fury’s Opponents” in 3 games v Fury)

Throws per Goal range from 30.80 (Heist in 1 game v Nemesis) to 7.462 (Traffic in 1 game v Fury)

For teams with more than 2 games, Thr/G ranged from 17.103 (“Riot’s Opponents” in 3 games v Riot) to 9.795 (Riot in 3 games)

Now for some theories:

Brute Squad is better at taking the easy passes than other teams I’ve watched this season. Not just in Women’s, but in Men’s as well (I have watched on mixed game in 2016 and I didn’t watch all of it. We had to travel to the stadium at the US Open to watch the Women’s and Men’s finals). They have a tendency to take the pop-out (“dishy”) on shallow in-cuts which serves to change the angle of attack and get the disc to a thrower who will have seen the field before becoming the thrower. I think this will serve them well at Nationals. I’m not certain that ultimate requires “a crunch-time go-to option” the way basketball does. It might, but that said… it might more require a group of players willing to just be simply and do what the situation requires before any individual excellence comes into play. Then, when it is time for said excellence to shine, said excellence shall make all the difference. More like soccer (a weak link game) than basketball (a strong link game). A team needs game-breakers on offense and defense in ultimate, but would be wise to refrain from over-reliance on the top of the roster.

One way to compare a team’s aggression (taking shots) to their ability to possess (throws per possession) is to subtract Throws/Goal from Throws/Possession. For every team which fails to complete 100% of their passes, this number will be negative. In the Women’s division (in the games I saw on tape) these numbers ranged from -2.612 to -25.490. Of teams with 2 or more games, Riot was the leader at -4.633. The next closest? Fury at -7.267 (Followed by Molly at -7.472, Brute at -7.827, Scandal at -8.032, Schwa at -10.270, and Nightlock at -11.690). The distance between Riot and the rest of the group is notable. The interesting bit is should teams just shoot earlier in their possessions or must they develop more comfortable ways of maintaining possession until ideal shots present themselves?

A way to compare these is to use Throws/EZ to (and Shooting %) to determine the efficacy of Shots and the frequency with which teams take/generate shots at the endzone. But… I’ll leave that alone for now. I shall leave you, dear reader, with a decent amount of this data to sift through yourself. I can type theories on this all night and day and on, but I need to pack for my trip to watch #NationalsIL (So Ill!), so here are some groupings of data. I double-checked the underlying data (and speadsheet math) repeatedly, but I could well have messed up some things. I did my best. I hope they help illuminate your understanding of 2016 USAU Women’s Ultimate!

9 comments:

Like with all of your posts, I have trouble reading every single word because there are so many, but the first thing I noticed was the break percentage in the second half (36%) was so much higher than in the first half (25%). I wonder if a team can get around this by playing deeper into the rotation and the strategic options in the first half instead of riding the same players and tactics until they start failing.

Sure sure... can't please 'em all... when I don't use words and replace them with numbers, I get complaints. When I type words to paint the numbers I get complaints... and "all of [my] posts"? Like all... two of them this year?

But to the point: I suspect (based on what I can recall from the videos) that the better teams in these games start to better leverage their advantages in the second half. I suspect this may have to do with the depth of talent on each team. That is the team most likely to win has a useful depth of 20 or more players whereas the team less likely to win has a useful depth of ~16 players (give or take).

I've been struggling with this as a coach this season in terms of how best to actively use the considerable depth of the team I coached rather than being forced to use depth or being constrained to using the same old strength over and over again. Similarly, the belief that what is working will continue to work is always dangerous. But changing too much or too soon is also dangerous.

Then again... I'm but a neophyte as a frisbee coach. I have tons to learn.

Most of these games felt rather close in the first half, but in the second, the tune changed and the team which would eventually win pulled away rather than letting the team who was behind change things up and get into a new rhythm/pattern. Haven't quite found a way to quantify that... but so it goes.

Do the winning teams also get broken more in the second half? In close games, does the break percentage go up? If it's that the deeper team pulls away in the second half by racking up several breaks, that's one possible explanation. But it feels like I've been in a bunch of games where neither team gets broken much in the first half but a lot in the second half, whereas games that start sloppy but get cleaned up are rare. O players get tired and make lazy cuts or don't cut at all, or run the same play one too many time. Half of defense is just being close enough to catch any swill.